Optical satellite sensors can provide a great deal of information for various global applications. However, cloud cover can cause a serious problem for these sensors, especially over humid tropical regions. Throughout the year about two thirds of the Earth's surface is covered by clouds. A problem for an optical sensor is that clouds not only conceal the ground, they also cast shadows and these shadows also occur in the observed images along with the clouds. Unlike airborne imaging where shadows can be minimized by flying at optically advantageous times, low Earth orbit satellite-based sensors are limited to acquiring images at fixed times of the day. If the solar elevation is low at the time, then the presence of shadow is increased. Cloud shadows can either reduce or eliminate useful information in an image. Reduction of information could potentially lead to the corruption of biophysical parameters derived from pixels values. Cloud shadow can produce errors of 30-40% in the observed reflectance from the affected pixels. Since ocean color products are retrieved based on the assumption that the remote sensing reflectances are accurate, a small inaccuracy in the reflectance can lead to significant errors in the retrieved products. Particularly, since most of the product retrieval algorithms are band ratio algorithms, a small disproportionate alteration in the spectral reflectance amplitude can changes the band ratios considerably, hence the retrieved products can be affected. If cloud shadows are not removed appropriately, shadow contamination can become a source of substantial defects in the clear-sky products and may introduce systematic biases in long-term data records.
Cloud shadow detection in ocean color scene can be important and beneficial. For example, the cloud shadowed pixel (pixel illuminated by only skylight photons since direct photons are removed by the cloud) in combination with the neighboring sunlit pixel (pixel illuminated by both direct solar and skylight photons) of similar optical properties can be used to remove atmospheric effects from these scenes. The neighboring sunlit pixels then can be used as known reflectance targets for validation of the sensor calibration and atmospheric correction. Cloud shadow is important for many other reasons as well. For example, cloud shadow can impact mesoscale atmospheric circulations that lead to major convective storm systems. Furthermore, cloud shadow can also be used to estimate both cloud base and cloud top height which are still a challenge to estimate reliably from space. Thus, it can be important to detect not only clouds but also their shadows from satellite images obtained from, for example, but not limited, high spatial resolution systems.
The locations of cloud shadows in the image depend on the cloud elevations and the incidence angle of the sunlight at the time of day of the measurement. The location of cloud shadows can be determined by the means of geometric calculations if the spatial location of cloud, cloud top and bottom heights, and the sun and satellite positions are known. However, geometry based cloud shadow location can be computationally expensive, and an estimation of a cloud's vertical height is required to determine the cloud shadow location. Thermal channels can be used to estimate the cloud's vertical height, but the determination of a cloud's bottom height can require cloud profiling measurements. Solar reflective bands cannot provide information about the cloud top height, and the cloud bottom information cannot be reliably estimated from passive solar-thermal data either. To determine accurate cloud shadow location, both heights are important, especially for isolated clouds. In any event, many ocean color sensors such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) don't have necessary channels to estimate clouds vertical heights.
In comparison to identifying cloud regions using brightness thresholds from ocean color sensor data, it is difficult to identify the cloud shadow regions because their brightness values are very close to those of their neighbors or some other regions. Distinguishing cloud shadows over water bodies based on spectral reflectance shape and amplitude information can be very difficult. Shadows' brightness varies with atmospheric conditions and imaging geometry. Brightness or the spectral shape alone may not be appropriate for cloud shadow detection. However, brightness values from cloud shadows and nearby sunlit regions over water can provide a great deal of information if a small portion of the image (where water and atmosphere can be assumed uniform) is examined at a time. Radiance measured over the water pixels (sunlit pixels) includes three components—radiance due to direct solar light, radiance due to diffuse sky light, and path radiance which accounts for most of the signal measured over water pixels. For the shadow pixels, radiance includes the reflection of the diffuse sky light and the path radiance. Path radiance from cloud shadow pixels to the sensor may be slightly lower than path radiance from sunlit pixels depending on how much of the path radiance atmosphere is shadowed. Water leaving radiance from the shadowed region is slightly lower than the nearby sunlit region since the shadowed region is only illuminated by the diffused sky light. The total top of the atmosphere (TOA) radiance from the shadow region should be slightly lower compared to the adjacent sunlit region. Assuming the optical properties of water and atmosphere are homogeneous around shadow and adjacent sunlit regions, studying small uniform regions one at a time can enable the separation of the cloud shadows from surrounding features.
Existing cloud shadow detection methods typically detect cloud shadows over land, and the geometry-based methods are computationally expensive. Also these methods are not applicable to visible sensors. What are needed are a system and method to identify the cloud shadow locations from air-borne and space-borne sensors, the system and method using visible channels since these channels are always present on ocean color sensors.